A systematic review of the prevalence of lifetime experience with ‘conversion’ practices among sexual and gender minority populations
Bibliographic record
Abstract
RATIONALE: Conversion practices (CPs) refer to organized attempts to deter people from adopting or expressing non-heterosexual identities or gender identities that differ from their gender/sex assigned at birth. Numerous jurisdictions have contemplated or enacted legislative CP bans in recent years. Syntheses of CP prevalence are needed to inform further public health policy and action. OBJECTIVES: To conduct a systematic review describing CP prevalence estimates internationally and exploring heterogeneity across country and socially relevant subgroups. METHODS: We performed literature searches in eight databases (Medline, Embase, PsycInfo, Social Work Abstracts, CINAHL, Web of Science, LGBTQ+ Source, and Proquest Dissertations) and included studies from all jurisdictions, globally, conducted after 2000 with a sampling frame of sexual and gender minority (SGM) people, as well as studies of practitioners seeing SGM patients. We used the Hoy et al. risk of bias tool for prevalence studies and summarized distribution of estimates using median and range. RESULTS: We identified fourteen articles that reported prevalence estimates among SGM populations, and two articles that reported prevalence estimates from studies of mental health practitioners. Prevalence estimates among SGM samples ranged 2%-34% (median: 8.5). Prevalence estimates were greater in studies conducted in the US (median: 13%), compared to Canada (median: 7%), and greater among transgender (median: 12%), compared to cisgender (median: 4%) subsamples. Prevalence estimates were greatest among people assigned male at birth, whether transgender (median: 10%) or cisgender (median: 8%), as compared to people assigned female at birth (medians: 5% among transgender participants, 3% among cisgender participants). Further differences were observed by race (medians: 8% among Indigenous and other racial minorities, 5% among white groups) but not by sexual orientation. CONCLUSIONS: CPs remain prevalent, despite denouncements from professional bodies. Social inequities in CP prevalence signal the need for targeted efforts to protect transgender, Indigenous and racial minority, and assigned-male-at-birth subgroups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".